CAJ | 학술논문

针对齿轮故障特征信号具有强噪声背景、非线性、非平稳性特点，提出采用形态梯度小波对齿轮振动信号进行降噪。首先使用形态梯度小波把齿轮振动信号分解到多个尺度上，然后对各层的细节系数进行软阈值方法降噪处理，对经过处理后的小波系数进行重构。对降噪后的齿轮振动信号采用S变换多分辨率时频分析，能够从具有良好的时频分辨率的S变换谱图提取齿轮故障特征。通过仿真试验和故障轴承的信号分析证明，该方法具有短时傅里叶变换和小波变换的优点，不存在Wigner-Ville分布的交叉干扰和负频率，能有效地提取隐含在噪声中的齿轮故障特征，适合齿轮故障的在线监测和诊断。
침대치륜고장특정신호구유강조성배경、비선성、비평은성특점，제출채용형태제도소파대치륜진동신호진행강조。수선사용형태제도소파파치륜진동신호분해도다개척도상，연후대각층적세절계수진행연역치방법강조처리，대경과처리후적소파계수진행중구。대강조후적치륜진동신호채용S변환다분변솔시빈분석，능구종구유량호적시빈분변솔적S변환보도제취치륜고장특정。통과방진시험화고장축승적신호분석증명，해방법구유단시부리협변환화소파변환적우점，불존재Wigner-Ville분포적교차간우화부빈솔，능유효지제취은함재조성중적치륜고장특정，괄합치륜고장적재선감측화진단。
The gear fault features are characterized with strong noise background, nonlinearity and non-stationary. The morphological gradient wavelet (MGW) is developed to eliminate noise. The gear vibration signal is decomposed by MGW, the detailed coefficients in each scale are processed using soft threshold de-nosing and the real fault signal is obtained by reconstructing the processed wavelet coefficients. Multi-resolution S transform is employed to analyze the reconstructed vibration signal, the gear fault features can be extracted from the spectral graphs generated from S transform are with good time and frequency resolution. Simulation and experiment results show that the proposed method can preserve the merits of short- time Fourier transform and wavelet transform, and does not involve the problem of negative frequency and cross frequency disturbances, which can extract the gear fault features effectively and is suitable for online gear monitoring and fault diagnosis.